49 citations · 412 across the 50 of their papers we have counts for
11 papers · 1 filter
A PAC-Bayesian Perspective on Structured Prediction with Implicit Loss Embeddings
Théophile Cantelobre, Benjamin Guedj, María Pérez-Ortiz +1
Many practical machine learning tasks can be framed as Structured prediction problems, where several output variables are predicted and considered interdependent. Recent theoretica…
Upper and Lower Bounds on the Performance of Kernel PCA
Maxime Haddouche, Benjamin Guedj, John Shawe-Taylor
Principal Component Analysis (PCA) is a popular method for dimension reduction and has attracted an unfailing interest for decades. More recently, kernel PCA (KPCA) has emerged as…
An end-to-end data-driven optimisation framework for constrained trajectories
Florent Dewez, Benjamin Guedj, Arthur Talpaert +1
Many real-world problems require to optimise trajectories under constraints. Classical approaches are based on optimal control methods but require an exact knowledge of the underly…
Cluster-Specific Predictions with Multi-Task Gaussian Processes
Arthur Leroy, Pierre Latouche, Benjamin Guedj +1
A model involving Gaussian processes (GPs) is introduced to simultaneously handle multi-task learning, clustering, and prediction for multiple functional data. This procedure acts…
Forecasting elections results via the voter model with stubborn nodes
Antoine Vendeville, Benjamin Guedj, Shi Zhou
In this paper we propose a novel method to forecast the result of elections using only official results of previous ones. It is based on the voter model with stubborn nodes and use…
MAGMA: Inference and Prediction with Multi-Task Gaussian Processes
Arthur Leroy, Pierre Latouche, Benjamin Guedj +1
A novel multi-task Gaussian process (GP) framework is proposed, by using a common mean process for sharing information across tasks. In particular, we investigate the problem of ti…